Shortage of gasoline is a common phenomenon during onset of forecasted disasters like hurricanes. Prediction of future gasoline shortage can guide agencies in pushing supplies to the correct regions ...and mitigating the shortage. We demonstrate how to incorporate social media data into gasoline supply decision making. We develop a systematic approach to examine social media posts like tweets and sense future gasoline shortage. We build a four-stage shortage prediction methodology. In the first stage, we filter out tweets related to gasoline. In the second stage, we use an SVM-based tweet classifier to classify tweets about the gasoline shortage, using unigrams and topics identified using topic modeling techniques as our features. In the third stage, we predict the number of future tweets about gasoline shortage using a hybrid loss function, which is built to combine ARIMA and Poisson regression methods. In the fourth stage, we employ Poisson regression to predict shortage using the number of tweets predicted in the third stage. To validate the methodology, we develop a case study that predicts the shortage of gasoline, using tweets generated in Florida during the onset and post landfall of Hurricane Irma. We compare the predictions to the ground truth about gasoline shortage during Irma, and the results are very accurate based on commonly used error estimates.
Panic-buying and shortages of essential commodities is common during early phases of a disaster or an epidemic. The goal of this paper is develop a methodology which includes social media information ...in optimization models of searching essential commodities during disasters and improves the efficiency of search. Specific contributions in the data processing of social media posts include the development of an event localizer that probabilistically infers the location and time of shortage of commodity based on social media information. Contributions in the mathematical model development include an integer programming formulation of the resultant search problem on a graph, with the two objective different objective functions: (a) Maximizing probability of finding the commodity (b) Minimizing expected time to find the commodity given the commodity is found. The methodology is validated using a case study on gasoline search during the Hurricane Irma evacuations. We found that social media posts can predict shortage at gas station for four major cities of Florida accurately with a MAPE of 12%. We also found that addition of social media information to the search process improved the average search time by 41.74%.
•Optimization model for an efficient search of an essential commodity.•Can social media posts can significantly improve the search efficiency?•Efficient processing of social media posts for use in a mathematical model.•Gasoline search in Hurricane Irma evacuation.
The manuscript titled “Kyasanur Forest Disease: Clinical Manifestations and Molecular Dynamics in a Zoonotic Landscape” explores the complexities of Kyasanur Forest Disease (KFD), a tick-borne viral ...infection caused by the Kyasanur Forest disease virus (KFDV). It delves into the clinical symptoms, transmission dynamics, pathogenesis, and the challenges of diagnosis and treatment, highlighting the disease's prevalence in specific regions of India and its expansion into new areas. The study emphasizes the importance of understanding the molecular structure of KFDV for the development of effective vaccines and therapeutic interventions. It also addresses the global health implications of KFD, underscoring the necessity for interdisciplinary collaboration, early detection, and preventive measures to manage and mitigate the impact of this and similar zoonotic diseases. Through a comprehensive analysis, the paper aims to enhance the scientific community's knowledge on KFD, contributing to global efforts in combating emerging infectious diseases.
The last mile delivery in humanitarian relief supply often happens on a tree or an almost-tree network allowing split deliveries. We present a relief delivery model incorporating a tree network for ...last mile delivery. We developed a mixed integer programming (MIP) formulation with the goal of minimizing the unsatisfied demand of the population. For better computational performance, we reformulated the MIP exploiting the tree network structure and found that this gave an order of magnitude reduction in computational time. To further improve computational efficiency, we developed a heuristic solution method based on a decomposition scheme applied to the tree network formulation. This led to the Capacitated Vehicle Routing Problem on trees with split deliveries, for which we derived a closed-form solution. This decomposition scheme resulted in a further order of magnitude reduction in computation time. To demonstrate the application of our approach we applied our model to the humanitarian logistics relief operation encountered in the 2015 Nepal earthquake.
Peripheral Tubercular Neuritis Agarwal, Amit; Pandey, S.D.; Khare, Abhinav
The Journal of hand surgery (American ed.),
20/May , Letnik:
48, Številka:
5
Journal Article
Recenzirano
Peripheral neuropathy can affect sensory, motor, or autonomic nerves and manifest with a variety of symptoms. Tuberculosis as a major infectious disease that often affects many organs of the body. ...However, primary involvement of peripheral nerves is unusual. Peripheral neuropathy in patients with tuberculosis often is associated with other comorbidities, such as immunocompromised states, diabetes mellitus, malnutrition, and some antitubercular medications. This report describes the rare finding of peripheral tubercular neuritis with caseating abscesses of right median and radial nerve in a healthy 24-year man.
Social media is extensively used by the affected public and responding organisations during disasters. This research work aims to assess the utility of social media data for making decisions related ...to essential-commodity shortages during disasters. Secondly, it develops a framework and methodologies to use social media data for making these decisions during disasters.The first chapter provides an introduction to the framework required for using social media data for decision making during disaster management. The second chapter develops a method to detect and forecast the shortage of essential commodities in a region using social media posts created within that region. The methodology is tested using a case study that forecasts the shortage of gasoline in eight different cities of Florida during the onset of Hurricane Irma. The third and fourth chapter progress to decision making problems i.e. optimisation problems that use information extracted from social media. Chapter three models the problem of search path planning for people searching for essential commodities during disasters. The information extracted in the second Chapter is included into the model and the solution method is tested for search path planning for evacuees looking for gasoline during Hurricane Irma.In chapter four, we develop a solution method for the problem of distribution of the inventory of an essential commodity when shortages are experienced at multiple locations. To minimise shortages, it proposes a solution for distribution of inventory from a warehouse to multiple demand locations and lateral transshipment between locations to share supplies according to future demand estimates. A reinforcement learning methodology, namely an asynchronous advantage actor critic model is developed to solve this problem. At every step (day), the reinforcement learning agent decides the distribution and sharing amounts in order to minimise shortage locations at all locations though a specified time period. A case study using numerical simulations is done to test the utility of this methodology for distribution and re-location of Personal Protective Equipment (PPE) across hospitals during scenarios like shortages of PPEs faced in COVID-19 pandemic.
A tragedy happened in Nepal in April 2015. A massive earthquake struck its capital, killing and injuring millions of people across the mountainous country. A large number of these people remained ...waiting for relief in remote and inaccessible regions in the mountains. To deliver relief items to these people the humanitarian logistics agencies setup an ingenious supply chain in which helicopters took the supplies to helicopter landing zones up to certain altitudes and the last mile delivery to the mountain villages was done by porters and pack animals traveling on mountain trails. In our study, we took up the challenge of optimizing this problem with the goal of minimizing the unsatisfied demand of the people in these villages and the cost of delivery. We developed two models for this problem. The first model is a multiperiod multimodal capacitated vehicle routing model. The second is a multiperiod multimodal capacitated vehicle routing model on trees also called the model with tree route formulation. The second model exploits the almost-tree shaped network of mountain trails in formulating the porter routing constraints. We solve both models by the branch-and-bound method. We show a considerable reduction in computation in the second method. The objective solution for the second model approximates well the solution of the first model as shown in the results. Furthermore, we develop a heuristic solution method for the second model. This method uses the multi-objective structure of our objective function to divide the model into multiple parts. It also takes advantage of the almost-tree shaped porter routing network. This method approximates the first model also as shown in the results. With this method, we show a further reduction in computation time of the solution.
The secondary renal involvement of non-Hodgkin lymphoma is frequently encountered but primary renal lymphoma is extremely rare in children. We hereby present a rare case of primary renal diffuse ...lymphoma in a 3-year-old girl child who presented with bilateral renal masses. The tumour was initially diagnosed as bilateral Wilms’ tumour but on biopsy, it turned out to be non-Hodgkin's lymphoma. Primary renal lymphoma is an aggressive tumour with guarded prognosis. There are a variety of diseases which present as renal masses in children amongst which primary renal lymphoma is extremely rare. This article describes the clinical features and diagnostic features of primary renal lymphoma. Our report highlights the importance of keeping this neoplastic condition under consideration while evaluating children with renal mass .In order to distinguish primary renal lymphoma from other renal tumours, the authors have also described the clinical, imaging and pathological features of various other renal tumours prevalent in children.
Toward a Trustless Smart City: the #SmartME Experience Khare, Abhinav; Merlino, Giovanni; Longo, Francesco ...
2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)
Conference Proceeding
A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered by actually decentralizing any mechanism and process that can escape single-authority control ...and the grip of centrally-managed bureaucracies, while at the same time not relying on any single party trusting any other. In line with this key insight, in this paper, we have designed and implemented a decentralized, trustless system for (environmental) sensing data acquisition, storage, and consumption, in the context of a real-world Smart City deployment and with the participation of independent, institutional, stakeholders. This Open Data system has been extended with an end-user wizard for user-friendly and trustless (i.e., independent) data audit. The acquisition layer requires nodes to authenticate when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party.
PDF